PubAg

Main content area

From woody cover to woody canopies: How Sentinel-1 and Sentinel-2 data advance the mapping of woody plants in savannas

Author:
Zhang, Wenmin, Brandt, Martin, Wang, Qiao, Prishchepov, Alexander V., Tucker, Compton J., Li, Yunmei, Lyu, Heng, Fensholt, Rasmus
Source:
Remote sensing of environment 2019 v.234 pp. 111465
ISSN:
0034-4257
Subject:
Internet, Sahel, canopy, ecosystem services, ecosystems, forests, livelihood, monitoring, remote sensing, satellites, savannas, support vector machines, temporal variation, time series analysis, trees, woody plants
Abstract:
Woody vegetation is a central component of savanna ecosystems providing ecosystem services for local livelihoods. Accurate monitoring of woody vegetation in savannas is therefore desirable, yet large scale mapping approaches rely on relatively coarse spatial resolution satellite data, which cannot directly capture the scattered nature of savanna trees. Studies at regional scale thus estimate the fractional cover of woody plants for a given area, whereas binary tree/no-tree estimates are restricted to the use of very high-resolution (VHR) images at local scales. With the launch of the Sentinel satellite systems (Sentinel-1 and Sentinel-2), the spatial resolution of images approaches the size of medium/large tree crowns, providing the opportunity to map the presence/absence of tree canopies, rather than the fraction of woody cover or forested areas. Here, we used a support vector machine (SVM) to classify the presence/absence of woody canopies from Sentinel-1 and Sentinel-2 data at a 10-m spatial resolution for the entire African Sahel. Training samples for the SVM classifier were collected from VHR images provided by Google Earth and Sentinel satellite data were processed in Google Earth Engine. Accuracy assessment was performed based on independent VHR images, showing an overall accuracy of 93% (71% and 98% for producer’s accuracy of woody and non-woody pixels, 91% and 93% for user’s accuracy of woody and non-woody pixels) when combining Sentinel-1 and Sentinel-2 data (overall accuracy of 89% using Sentinel-1 only and 91% using Sentinel-2 only). The combined use proved to perform significantly better (p < 0.05) than the single use of any of the two. A comparison with existing tree cover maps (by aggregating presence/absence of tree canopies into fractional cover) showed noticeable differences, reflecting the need for new woody cover products adapted to the nature of savanna ecosystems. The Sentinel woody canopy map was able to reproduce the general pattern of scattered woody canopies, but generally overestimated the woody coverage (11.37 ± 26.13% (mean ± sd) when aggregating to 250 m resolution) due to the 10 × 10-m spatial resolution which exceeds the crown size of a typical savanna tree. The cloud-based Sentinel-1 and Sentinel-2 analysis presented is a step towards large scale mapping of woody canopy (tree/no-tree) in savannas. Ultimately, such direct assessment of woody canopy areas will allow monitoring of temporal dynamics of woody canopies in future studies as Sentinel time-series expands to multiple years.
Agid:
6721485